Introduction to Synthetic Data for Machine Learning
Chapter 1: Understanding Synthetic Data
What is Synthetic Data?
Why Generate Artificial Data?
Real Data vs. Synthetic Data
Common Terminology
Potential Benefits
General Limitations
Quiz for Chapter 1
Chapter 2: Basic Methods for Data Generation
The Idea of Data Generation Models
Generating Data from Statistical Distributions
Introduction to Rule-Based Systems
Generating Simple Numerical Data
Generating Simple Categorical Data
Hands-on Practical: Create Basic Synthetic Data
Quiz for Chapter 2
Chapter 3: Generating Synthetic Tabular Data
Understanding Tabular Data Structure
Row Sampling Techniques
Independent Column Value Generation
Preserving Basic Column Correlations
Introduction to Data Anonymization Concepts
Hands-on Practical: Generate a Synthetic Table
Quiz for Chapter 3
Chapter 4: Introduction to Synthetic Image Data
Why Synthetic Data for Images?
Basic Image Properties: Pixels and Color
Creating Images with Simple Shapes and Patterns
Applying Noise and Simple Augmentations
Introduction to Rendering Simple Scenes
Challenges in Realistic Image Generation
Hands-on Practical: Generate Simple Synthetic Images
Quiz for Chapter 4
Chapter 5: Evaluating Synthetic Data Quality
Importance of Evaluation
Visual Inspection Methods
Basic Statistical Comparisons
Comparing Data Distributions
Concept of Fidelity vs. Utility
Quiz for Chapter 5
Chapter 6: Tools and Libraries Overview
Role of Software in Data Generation
Libraries for Basic Data Manipulation (NumPy, Pandas)
Introduction to Faker Library
Libraries for Simple Image Manipulation (Pillow, Scikit-image)
Finding Generation Tools
Quiz for Chapter 6
Row Sampling Techniques
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- Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, Peter Bruce, Andrew Bruce, and Peter Gedeck, 2020 (O'Reilly Media) - Presents statistical sampling techniques, including random and stratified methods, relevant for data science and machine learning.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - A standard text on statistical learning. It covers resampling methods, such as bootstrapping (sampling with replacement), and data splitting, which are useful for row sampling in machine learning.
- sklearn.model_selection.StratifiedShuffleSplit, scikit-learn developers, 2023 (scikit-learn) - Official documentation for stratified sampling in machine learning. It shows practical implementation for keeping class proportions during data splitting.
- Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber, Jian Pei, 2011 (Morgan Kaufmann) DOI: 10.1016/C2009-0-61819-5 - A textbook on data mining. It discusses data preprocessing and reduction, covering basic sampling techniques (e.g., random sampling, stratified sampling) useful for data preparation and synthetic dataset creation.